Topic:"Load Management" in M10569

REDACTED (CONFIDENTIAL INFORMATION REMOVED) Nova Scotia Utility and Review Board IN THE MATTER OF The Public Utilities Act, R.S.N.S. 1989, c.380, as amended 2022 Load Forecast Report April 29, 2022 REDACTED REDACTED (CONFIDENTIAL INFORMATI...

AI summary The document is a redacted 2022 Load Forecast Report submitted under the Public Utilities Act, R.S.N.S. 1989, c.380, as amended, relating to a regulatory proceeding by the Nova Scotia Utility and Review Board. Key details are confidential.

k (including DR) ..................................................... 84 30 Figure 58: Peak Contribution Components (MW)........................................................................ 85 DATE: April 29, 2022 Page 4 of 98 REDACTED...

AI summary The 2022 Load Forecast Report includes figures analyzing peak demand contributions, forecast accuracy, weather-normalized firm peak data, residential and commercial end-use peak shares, load research data comparisons, energy/peak sensitivity, and Integrated Resource Plan (IRP) scenario comparisons, focusing on load forecasting methodologies and demand response integration.

Page 6 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The document is a redacted excerpt from the 2022 Load Forecast Report, part of a Nova Scotia regulatory proceeding. Confidential information has been removed, limiting details about load forecasting methodologies, assumptions, or projections related to energy demand.

ecast annual increase of 0.3 percent. 14 Annual historic and forecast NSR are shown below in Figure 1. 15 16 Figure 1: Historical and Predicted Annual Net System Requirement 17 18 DATE: April 29, 2022 Page 8 of 98 REDACTED (CONFIDENTIAL IN...

AI summary NS Power forecasts a 0.3% annual increase in net system requirement and 1.6% annual growth in system peak demand, driven by customer growth and electric heating, partially offset by demand-side management (DSM) activities. Historical and projected data are visualized in Figures 1-3.

relationship of the Load Forecast to the Company’s evergreen 27 IRP modeling update, the EV model assumptions, vehicle-to-grid technology, and 28 customer growth in the province. 29 DATE: April 29, 2022 Page 14 of 98 REDACTED (CONFIDENTIAL...

AI summary NS Power collaborated with E3 to develop load forecasts for space heating and EV uptake under the 2020 IRP Action Plan, aiming to meet emissions goals and EV targets. The analysis uses stock rollover models and was presented to stakeholders in April 2022.

ural changes are captured in 22 the residential forecast model through the SAE model specifications. Figure 4 shows the 23 general forecast approach used in the SAE models. 24 3 References to the Residential class include Domestic Service...

AI summary The residential forecast model incorporates SAE specifications, with Figure 4 illustrating the general forecast approach used in SAE models. The document includes class definitions for Residential, Commercial, and Industrial categories.

Figure 7: HDD Trend 8 9 10 DATE: April 29, 2022 Page 21 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 8: CDD Trend 2 3 4 5 These trends are reduced over time (approximately 40 years) such tha...

AI summary The 2022 Load Forecast Report discusses trends in Heating Degree Days (HDD) and Cooling Degree Days (CDD) over a 10-year period, showing a reduction in HDD and an increase in CDD. These trends affect winter heating and summer cooling loads for residential and commercial classes, with fluctuations in specific years due to leap years.

NTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 9: Annual HDD and CDD Over Time 2 3 4 5 With respect to peak temperatures, both the morning (7am – 10am) and evening peak 6 period (5pm-8pm) annual minimum temperatures...

AI summary The 2022 Load Forecast Report discusses the evaluation of peak temperature periods, focusing on annual minimum temperatures during morning and evening peaks. It notes that only two annual peaks occurred in the morning peak period over the past 20 years, despite colder temperatures, and uses the average of the previous 10-year evening peak period minimum as an indicator of future peak conditions.

EDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 12: County Grouping 2 3 4 5 Figure 13 below shows the details of the weightings. 6 DATE: April 29, 2022 Page 27 of 98 REDACTED (CONFIDENTIAL INFORMATION...

AI summary The 2022 Load Forecast Report includes figures related to county grouping and weather station weighting, with specific details redacted due to confidentiality.

ED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 13: Weather Station Weighting 2

AI summary The document includes a redacted figure titled 'Figure 13: Weather Station Weighting' from the 2022 Load Forecast Report. The content is confidential and has been removed.

5 Figure 14 below show forecast MAPE comparisons between model weather dependent 6 customer classes and accrued system peak. 7 8 Figure 14: Forecast Results 9 Class MAPE (1 station) MAPE (multiple stations) Difference Residential 2.58% 2.5...

AI summary The text compares forecast MAPE results between different customer classes and system peak, noting minimal differences that did not impact the 2022 Load Forecast. Economic data from the Conference Board of Canada is used, and the residential model was updated to use household compensation instead of retail sales and disposable income.

D (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 17: Residential Economic Drivers 2

AI summary The document presents a redacted section of the 2022 Load Forecast Report, focusing on residential economic drivers, though key details have been removed due to confidentiality.

-9.6 0.7 3 4 DATE: April 29, 2022 Page 32 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 18: Commercial Economic Drivers 2

AI summary The document includes a redacted section of the 2022 Load Forecast Report, specifically Figure 18, which discusses commercial economic drivers. The content has been redacted due to confidentiality.

NFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 19: Industrial Economic Drivers 2

AI summary The text refers to a 2022 Load Forecast Report, with Figure 19 focusing on industrial economic drivers. However, the content is redacted, and no further details are provided.

1.1 34 0.4 12‐21 0.3 -0.1 22‐32 2.0 0.5 3 DATE: April 29, 2022 Page 34 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The document is a redacted section of the 2022 Load Forecast Report, which provides an overview of load forecasting data and analysis. Specific details are omitted due to confidentiality.

OVED) 2022 Load Forecast Report REDACTED 1 Figure 22: Commercial Space Heating Saturation Comparison 2 3 4 5 E3 also estimates peak impacts associated with increased electric space heating in both the 6 residential and commercial sectors....

AI summary E3 estimates peak impacts from increased electric space heating in residential and commercial sectors, noting that the SAE model underestimates peak demand compared to E3's building stock model. Adjustments are made to account for this discrepancy, with 25% and 20% already captured in residential and commercial classes, respectively.

ions and changes to saturation and 17 intensity over the forecast period. Work is underway to collect more detailed data on the 18 contribution of heat pumps to load and peak.11 19 11 NS Power Annual and Regulated Financial Statements – On...

AI summary The document discusses ongoing work to collect more detailed data on the contribution of heat pumps to load and peak demand, with a reference to a study update submitted by NS Power to the UARB in January 2022.

2022 Load Forecast Report REDACTED 1 Figure 28: EV Impact to Energy and Peak Forecasts (cumulative) 2

AI summary The 2022 Load Forecast Report includes a figure illustrating the cumulative impact of electric vehicles (EVs) on energy and peak forecasts. The figure is labeled as Figure 28 and is part of the redacted content.

Peak @ Peak @ Load Year EVs 0.9kW/vehicle 1.3kW/vehicle (GWh) (MW) (MW) 2022 2,864 12 2 4 2023 5,978 26 5 8 2024 10,258 48 9 14 2025 15,680 76 14 21 2026 22,232 110 20 30 2027 29,908 153 27 40 2028 38,671 204 35 52 2029 48,465 259 45 66 20...

AI summary The text provides a forecast of peak load and electric vehicle (EV) growth from 2022 to 2032, along with information on solar generation in Nova Scotia. It highlights the discrepancy between forecasted and actual solar installations and their impact on residential load reduction.

Page 47 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The document is a redacted version of the 2022 Load Forecast Report, which provides an analysis of electricity demand projections for the year 2022. The report is part of a regulatory proceeding and includes confidential information that has been removed.

oject. 19 These are illustrative estimates based on limited data sets and will be refined as the project 20 continues. 21 22 Figure 30: Potential Peak Impacts from Batteries 23 Residential Share (%) Technology 50% 25% 10% 5% Battery Peak I...

AI summary The document provides illustrative estimates of potential peak impacts from battery technologies under different control scenarios, including no control and optimal demand response control, as part of the 2022 Load Forecast Report.

Page 48 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The 2022 Load Forecast Report provides an analysis of projected electricity demand, incorporating factors such as weather patterns, economic trends, and energy efficiency initiatives. The report includes redacted information and is part of a regulatory proceeding.

ell as smaller appliances such as computers, dehumidifiers, 28 microwaves, etc. This category also includes solar generation (photovoltaic or PV) 29 and EV forecasts. 30 DATE: April 29, 2022 Page 49 of 98 REDACTED (CONFIDENTIAL INFORMATION...

AI summary The document discusses residential and commercial end-use intensities, including trends in heating, cooling, and appliance usage. It highlights the increasing use of heat pumps and the impact on energy demand, as well as the slow decline in lighting and refrigeration due to improved efficiency. Supporting data is referenced in an attachment.

To address the issue of double counting, the approach used is the same as that used in prior 24 forecasts: to introduce cumulative historical DSM savings as reported by E1 to the 25 regression model as a load modifying variable, and allow...

AI summary The text discusses the approach to address double counting in load forecasts by using cumulative historical DSM savings as a load modifying variable in a regression model. It references prior regulatory decisions and filings related to DSM resource plans and efficiency programs.

Page 60 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The document contains a redacted section of the 2022 Load Forecast Report, which is likely related to energy demand projections and analysis for Nova Scotia. The content has been confidentially removed and is not visible in the provided text.

. Details of both building efficiency and house size were included in the 19 end use survey, and analysis is ongoing. Future surveys will help to identify trends in this 20 area. 21 DATE: April 29, 2022 Page 61 of 98 REDACTED (CONFIDENTIAL...

AI summary The document discusses ongoing analysis of building efficiency and house size data from an end use survey, with future surveys expected to identify trends. It also references a 2022 Load Forecast Report, including figures that show changes in building characteristics and structural indices over time.

2022 Load Forecast Report REDACTED 1 Figure 40: Residential Sales Components by Year 2

AI summary The text references a 2022 Load Forecast Report and includes a figure titled 'Residential Sales Components by Year', though the content is redacted and no further details are provided.

-281 4,862 -549 -268 2031 4,730 268 -160 403 -3 -315 4,922 -616 -300 2032 4,764 282 -176 510 -3 -349 5,027 -681 -332 3 4 Figure 41 provides an approximation of the heat pump heating, heat pump cooling, electric 5 baseboard, and water heate...

AI summary The text discusses the methodology used to approximate system-level loads from heat pumps, electric baseboard heating, and water heaters using regression models and data from the 2020 Load Forecast. It notes that these numbers are illustrative and do not include DSM amounts or account for potential differences in how X variables apply to various end uses.

RMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 41: Illustrative Contribution of Specific End Uses 2 Year HP Heat HP Cool Baseboard Heat Water Heat (GWh) (GWh) (GWh) (GWh) 2022 607 74 1096 710 2023 656 81 1059 725 2024 705 87...

AI summary The 2022 Load Forecast Report provides an illustrative breakdown of energy consumption by specific end uses, including heat pump heating and cooling, baseboard heating, and water heating, across the years 2022 to 2032. The data shows projected trends in energy usage for these categories over time.

Page 64 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The text refers to a 2022 Load Forecast Report, with confidential information redacted. The report likely discusses projected electricity demand for the year 2022, though specific details are not available due to redaction.

TION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 43 Commercial Sales vs Economic Indicators 2 3 4 5 6.1 Small General Service 6 7 Historical and forecast Small General service loads are shown in Figure 44. Small General 8 service...

AI summary The 2022 Load Forecast Report discusses historical and forecasted Small General Service loads, noting an average annual increase of 0.5 percent. Commercial electrification is expected to add 18 GWh by 2032, but this will be offset by demand-side management (DSM) and decreased intensity forecasts for ventilation, lighting, and miscellaneous end uses.

ED) 2022 Load Forecast Report REDACTED 1 Figure 44: Historical and Forecast Annual Small General Sales 2 3 4 5 Please refer to Appendix B for tables with a detailed breakdown of the changes from 2022 6 to 2032. Total change between 2022 an...

AI summary The 2022 Load Forecast Report indicates a 5.5 percent increase in total load from 2022 to 2032. General class load is projected to decline by 0.4 percent annually over the 10-year forecast period, with increased space heating partially offset by demand-side management (DSM) programs and improved efficiency in lighting and miscellaneous end uses.

2022 Load Forecast Report REDACTED 1 Figure 45: Historical and Forecast Annual General Demand Sales 2 3 4 5 Please refer to Appendix B for tables with a detailed breakdown of the changes from 2022 6 to 2032. Total change between 2022 and 2...

AI summary The 2022 Load Forecast Report indicates a projected 3.7% decrease in total demand from 2022 to 2032. The Large General Service class remained unchanged from 2020 due to the impacts of the COVID-19 pandemic, with decreased sales in sectors like retail, office, university, and transportation. Customer surveys and historical data are used to forecast demand, with flat load levels assumed in the absence of survey data.

98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 annual survey: two indicated no change, four indicated a decrease, seven indicated an 2 increase. Growth in this class is expected to be driven by institut...

AI summary The 2022 Load Forecast Report discusses the expected increase in electricity demand, driven by institutional facilities, particularly hospital expansions in Halifax and Sydney. The forecast projects an increase of approximately 50 GWh by 2032.

Page 70 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 7.0 INDUSTRIAL AND MUNICIPAL SECTORS 2 3 The forecast models for the Small Industrial and Medium Industrial classes are 4 econometric-based mode...

AI summary The 2022 Load Forecast Report discusses the Small Industrial class forecast, which is based on econometric models using provincial manufacturing GDP as the primary variable. Sales in this class have been flat over the past decade and are expected to grow at 0.7% annually due to underlying economic growth.

2022 Load Forecast Report REDACTED 1 Figure 48: Historical and Forecast Annual Medium Industrial Sales 2 3 4 5 7.3 Other Industrial Rate Classes 6 7 Other Industrial rate classes include Large Industrial, Large Industrial Interruptible, 8...

AI summary The report discusses the forecasting of load for various industrial rate classes, including Large Industrial and Extra Large Industrial, using customer surveys and historical sales data. Survey responses indicate mixed expectations for energy consumption changes, with some customers expecting increases, decreases, or no change.

Page 73 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 to historic levels in 2021 and are forecast to increase load by another in 2022. 2 Several new facilities or expansions are expected in the mini...

AI summary The 2022 Load Forecast Report indicates that load levels are expected to rise following historic levels in 2021. New industrial projects in mining, manufacturing, and processing sectors are anticipated to contribute significantly to load increases, with cumulative GWh values provided for 2022 through 2025. The report also references historical and forecast sales for the Other Industrial sector.

nd is included in the Load Forecast. The group’s forecast 23 energy sales have been reduced to reflect only the reduced 2021 energy purchases from NS 24 Power under the BUTU tariff. DATE: April 29, 2022 Page 75 of 98 REDACTED (CONFIDENTIAL...

AI summary The document discusses the 2022 Load Forecast Report, highlighting energy sales reductions due to decreased purchases from NS Power under the BUTU tariff, as well as system losses and unbilled sales. System losses are forecast to remain between 6.0% and 7.0% over the 10-year period. The Net System Requirement (NSR) includes residential, commercial, and industrial sales, excluding self-generation and exports.

AL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 52: Historical and Forecast Annual NSR 2 3 4 5 Figure 53 provides a breakdown of the various components of the change in the forecast 6 from 2022 to 2032. Data for all cla...

AI summary The 2022 Load Forecast Report provides historical and forecast data on Net System Requirement (NSR) and its components, including residential, commercial, industrial, and other load categories, from 2022 to 2032. It details factors influencing the forecast, such as new customers, solar adoption, electric vehicle growth, and demand-side management (DSM) impacts.

Page 79 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED

AI summary The document is a redacted section of the 2022 Load Forecast Report, which provides an analysis of electricity demand projections for the year 2022. Key details have been removed due to confidentiality.

2022 Load Forecast Report REDACTED 1 Annual DR totals by program are provided in Figure 54. 2 3 Figure 54: Demand Response 4

AI summary The text references Figure 54, which provides annual Demand Response (DR) totals by program from the 2022 Load Forecast Report. The figure is redacted, so no further details are available.

Year Direct Critical Business, Total Total Load Peak Non-Profit (MW) with Control Pricing & Industrial ELCC (MW) (MW) Curtailment (MW) (MW) 2022 0 1 0 1 0 2023 4 4 1 9 4 2024 12 12 2 26 12 2025 24 22 4 50 24 2026 36 32 6 74 36 2027 39 36 7...

AI summary The text presents a table showing the implementation of Direct Load Control (DLC) and Critical Peak Pricing (DR) programs across various years, highlighting the growth in capacity and participation. A pilot project with E1 is underway to test water heater controls, with early results indicating potential peak savings.

m one group of pilot participants indicate that an average 10 reduction of 0.5 kW of peak savings per unit is achievable. 11 12 NS Power is also working with E1 on a two-phased pilot project to investigate automatic 13 and manual control o...

AI summary NS Power is conducting pilot projects with E1 to explore automatic and manual load control for commercial and industrial customers, aiming to achieve peak savings and develop demand response (DR) capacity. Data from these projects will be used to improve forecast assumptions.

Page 81 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Load Forecasts. The impact of these initiatives, at least within the 10-year timeframe of 2 this forecast, is expected to fall within the sensit...

AI summary The 2022 Load Forecast Report discusses the impact of demand response (DR) programs on load forecasts, noting that DR programs do not inherently reduce demand but can be used as a resource during peak times. The report also explains the change in assumed peak temperature from -15 to -13.7 degrees Celsius based on a 10-year average of coldest evening temperatures.

N REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 56: Historical and Forecast System Peak (no DR) 2 3 4 5 Figure 56 also shows the January 2022 peak, which occurred at a temperature of -14.6 6 degrees C, and was very close to the fore...

AI summary The 2022 Load Forecast Report discusses historical and forecast system peak demand, noting the January 2022 peak at -14.6°C and a 1.5% annual increase in firm peak demand, which accounts for interruptible and demand response (DR) loads.

OVED) 2022 Load Forecast Report REDACTED 1 Figure 57: Historical and Forecast Firm Peak (including DR) 2 3 4 5 Forecast peak values, firm peak and interruptible peak information can be found in 6 Appendix A. As discussed in Section 4.4, th...

AI summary The 2022 Load Forecast Report discusses the projected growth in peak demand, including contributions from electric vehicles, space heating, and various customer classes. The report highlights the impact of managed charging on EV peak demand and the expected increase in residential and commercial heating demand by 2032.

Modeled Res EV DR C&I Large DSM Firm Inter. System Peak Heat (MW) (MW) Elect. Cust. (MW) Peak Cust. Peak (MW) Peak (MW) (MW) (MW) (MW) (MW) (MW) 2022 1,920 7 3 -0 10 99 -18 2021 144 2,165 2032 1,993 120 103 -37 193 112 -141 2342 152 2,532...

AI summary The document discusses system peak demand in Nova Scotia, highlighting the 2021 system peak of 1,968 MW and the factors influencing peak demand, such as temperature changes and weather conditions. It also provides modeled data for 2022 and 2032, including the impact of EV adoption and demand response programs.

a combination of day of week, time of day, temperature, and 20 weather conditions at both an hourly and daily level; as a result, the peak compared to 21 forecast will be more variable than energy (which considers longer time frames). Figu...

AI summary The 2022 Load Forecast Report discusses the variance between forecasted and actual system peak loads in 2021, highlighting factors such as interruptible load, weather, and unexplained differences. It also mentions the normalization of firm peak for weather and lighting load to align with historical trends.

Page 86 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 While the Load Forecast is a good statistical fit for the historical data, it presents challenges 2 when trying to assess the contribution of in...

AI summary The 2022 Load Forecast Report discusses the statistical fit of the load forecast with historical data and challenges in assessing individual end-use contributions to peak demand. It highlights that residential and commercial end-use contributions remain stable, though EV usage is projected to increase to 4.6% due to electrification. Electric heating's impact on peak demand will be studied further through a heat pump monitoring project.

vs System Generation, 2021 Annual Peak 2 3 4 DATE: April 29, 2022 Page 90 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 64: 2021 Monthly Load Research Data vs System Generation 2 3 4 Losses c...

AI summary The 2022 Load Forecast Report discusses methods for estimating system losses by comparing load research sales with system generation, and highlights the inclusion of data from the 2020 and 2021 pandemic years in the residential class peak demand forecast analysis.

Page 91 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 The model does not need all 8760 hours available each year, only residential loads at the 2 hour of system peak of each month of the historical...

AI summary The document discusses the 2022 Load Forecast Report, focusing on residential load forecasting using historical data and the Load Research Sample (LRS). It mentions the use of a top-down model and the SAE Peak Demand equation, with Figure 65 illustrating historical data and forecasts.

2022 Load Forecast Report REDACTED 1 Figure 65: Monthly historical Residential LRS load at peak and forecasts 2 3 4 5 Both the current residential peak demand forecast (green line) and the LRS peak 6 experimental model (black line) are des...

AI summary The 2022 Load Forecast Report discusses residential peak demand forecasts and experimental models, noting improvements in summer cooling load resolution due to factors like heat pump proliferation and remote work. It highlights differences between top-down and bottom-up forecasting approaches, with discrepancies ranging up to 200 MW.

Page 93 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 The bottom-up approach, which relies on class-level load research, captures customer 2 behavior, at peak, more realistically. For instance, ther...

AI summary The 2022 Load Forecast Report discusses the use of a bottom-up approach for load research, highlighting its ability to capture customer behavior during peak times. It also mentions the potential of using AMI smart meter data to assess the impact of the pandemic on residential consumption and improve future load forecasts.

FIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 67: System Peak Sensitivity 2 3 4 This analysis provides a potential range of outcomes for the 2022 Load Forecast. Energy 5 is most sensitive to Economics over the...

AI summary The 2022 Load Forecast Report discusses the sensitivity of energy and peak demand to factors such as economics and temperature. It compares the 2021 and 2022 Load Forecasts with the 2020 IRP cases, noting similarities in outcomes despite new analyses on space heating and EV adoption. The report also mentions stakeholder discussions regarding assumptions about incentives affecting EV and heat pump uptake.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 the values represent a slow ramp-up of uptake of these technologies that would allow 2 interim sales targets as well as the target of net zero emissions by 20...

AI summary The 2022 Load Forecast Report outlines projections for energy demand, considering factors such as the uptake of electric vehicles (EVs) and electrification scenarios. The report reflects updated federal EV sales targets and discusses potential changes in policy and regulation over the next decade.

2.1% 3,091 4.0% 2,542 2.5% 70 -2.4% 726 11,144 2.2% 2023 4,682 -0.7% 3,135 1.4% 2,548 0.2% 70 0.0% 727 11,162 0.2% 2024 4,711 0.6% 3,143 0.3% 2,583 1.4% 70 0.1% 732 11,240 0.7% 2025 4,713 0.0% 3,142 0.0% 2,608 0.9% 70 -0.3% 732 11,265 0.2%...

AI summary The document provides a table of load forecast data for various years, showing percentages and numerical values related to demand forecasting. The data appears to be part of a 2022 Load Forecast Report Appendix A, specifically Table A2, which outlines coincident peak demand forecasts for NS Power.

Interruptible Demand Firm Net System Temp at Contribution to Response Contribution to Growth Peak Peak Year Peak (reduction in Peak Notes Firm Peak only, (%) MW) (MW) (deg C) (MW) (MW) - February 13 2012 141 1,740 1,882 -13.2 -7 weekday ev...

AI summary The table provides data on interruptible demand, firm peak contributions, and net system peak growth for various years, including reductions in firm peak and temperature at peak times. It outlines the contribution of demand response to peak load management and system growth over time.

March 2 weekday 2021 94 - 1,875 1,968 -4.0 -10 evening 2022 144 - 2,021 2,165 10.0 -13.7 Forecast 2023 146 -4 2,035 2,185 0.9 -13.7 Forecast 2024 146 -12 2,057 2,215 1.4 -13.7 Forecast 2025 152 -24 2,076 2,253 1.7 -13.7 Forecast 2026 154 -...

AI summary The document presents a load forecast report with data spanning from 2021 to 2032, detailing metrics such as load, capacity, and various percentages. The data includes forecasted values and percentages for different years, with some entries marked as 'Forecast'.

2022 Load Forecast Report Appendix B Page 4 of 32 Appendix B – Forecast Model Details Residential Model Statistics Model Statistics Iterations 21 Adjusted Observations 120 Deg. of Freedom for Error 106 R-Squared 0.990 Adjusted R-Squared 0....

AI summary This section provides statistical details for a residential load forecasting model, including metrics such as R-squared, adjusted R-squared, AIC, BIC, and other statistical indicators. It outlines model performance and assumptions used in the 2022-2032 residential load forecast reconciliation.

t Appendix B Page 5 of 32 Appendix B – Forecast Model Details Residential SAE Model Fit Residential Model 2022-2032 Reconciliation The following tables provide details reflecting the changes between 2022 and 2032 forecast years. Some of th...

AI summary The document provides a reconciliation of residential load forecasts between 2022 and 2032, showing changes in customer load, EV load, solar load, and DSM captured. It includes a table with data on existing and new customer usage, energy efficiency savings, and load adjustments.

382 Change 2.4% 62.8% 1.2% 10.7% 0.0% 71.8% to load XCool = (Central AC + HP Cool + Room AC) x CoolUseVariable x Coeff Page 6 of 31 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report Appendix B Page 8 of 32 Appendix B –...

AI summary The text provides details on residential and commercial load forecast models, including variables such as XCool and XOther, which are calculated using specific intensities and coefficients. The data shows changes in load intensity and usage variables over time, from 2022 to 2032.

22 Load Forecast Report Appendix B Page 11 of 32 Appendix B – Forecast Model Details Small General Model Statistics Model Statistics Iterations 13 Adjusted Observations 120 Deg. of Freedom for Error 110 R-Squared 0.947 Adjusted R-Squared 0...

AI summary This section provides statistics for a small general model used in load forecasting, including metrics such as R-squared, adjusted R-squared, AIC, BIC, and other statistical indicators. The report also includes a reconciliation for the 2022-2032 period.

22 Load Forecast Report Appendix B Page 14 of 32 Appendix B – Forecast Model Details Small General Input Variables – XCool Intensities Econ + Struct Regression Cooling CoolUse Variable Coefficient Scaling Factor Total XCool (kWh) 2022 36,4...

AI summary This section of the Load Forecast Report provides detailed input variables for the XCool and XOther components of the forecast model, including intensity values, economic and structural factors, regression coefficients, and scaling factors for the years 2022 and 2032. It highlights changes in these variables over the decade.

22 Load Forecast Report Appendix B Page 18 of 32 Appendix B – Forecast Model Details General Demand Load – Post Regression (GWh) Load NS Power Solar GD DSM Gen Sales Total DSM Regression C&I Adjustment (with DSM) GD captured Model Electrif...

AI summary The document presents a load forecast report focusing on general demand load and sales, with detailed regression models and input variables. It includes data for 2022 and 2032, highlighting changes in load, sales, and various demand-side management (DSM) factors.

2,375,439 Change 4.9% 0.4% -4.4% 0.0% 1.0% 2.0% Sales = XHeat + XCool + XOther + Binaries + ARMA General Demand Input Variables – WtXHeat Intensities Econ + Struct Regression Heating HeatUse Coeff Total XHeat Variable 2022 493,867 1.26 0.6...

AI summary The text discusses general demand input variables for heating and cooling, including intensities, economic and structural factors, regression coefficients, and scaling factors. It outlines how these variables are used in the load forecast model to calculate XHeat and XCool, with examples of their contributions to overall demand.

0.370 75,451 Change -4.3% 20.4% 0.0% 0.0% 16.1% XCool = Cooling x CoolUseVariable x Coeff x Scaling Factor General Demand Input Variables – XOther Intensities Econ Reg + Struct Vent Water Cook Refrig Light Office Misc Other Coeff Scaling T...

AI summary The document presents data and formulas related to energy demand forecasting, including variables such as XCool and XOther, which are calculated using intensity values, coefficients, and scaling factors. It outlines input variables for general demand and provides details on an industrial econometric model used for load forecasting.

Small Industrial model SmlInd_Salesm = MBin.Janm + MBin.Febm + MBin.Marm + MBin.Aprm + MBin.Maym + MBin.Junm + MBin.Julm + MBin.Augm + MBin.Sepm + MBin.Octm + MBin.Novm + MBin.Decm + b1×MEcon.ManGDP As discussed in Section 4.0, a historic...

AI summary This section presents the Small Industrial model used for load forecasting, including the equation for SmlInd_Salesm and the coefficients for various variables, such as monthly bins and GDP, with statistical significance values provided.

2022 Load Forecast Report Appendix B Page 28 of 32 Appendix B – Forecast Model Details Peak Forecast (Accrued Classes) The long-term system peak forecast for the accrued classes is derived through a monthly peak linear regression model tha...

AI summary The document details the methodology for forecasting long-term system peak demand using a monthly peak linear regression model that incorporates heating, cooling, and base load requirements. The model normalizes heating and cooling load requirements based on the number of days and hours in the month to estimate average MW load.

eather sensitive load drivers in each month of the year. OtherLoadm is comprised of: OtherLoadm=ResOtherm + SmlGSOtherm + GSOtherm + SmIndSalesm + MedIndSalesm + UnMSalesm Where ResOtherm, SmlOtherm and GSOtherm are the non-weather depende...

AI summary The text describes the decomposition of load drivers into weather-sensitive and non-weather-dependent components, including the use of a sales model with regression coefficients to isolate non-weather factors such as DSM activities. It also mentions normalization of load requirements and the use of a binary variable to account for the impact of the COVID-19 pandemic starting in 2020.

Appendix D – Forecast Sensitivity Analysis Figure D4: Peak Forecast (Residential, Commercial and Small and Medium Industrial) The asymmetry in this figure, seen as the off-centre median, is explained by the bias introduced by plotting the...

AI summary The document discusses the asymmetry in peak forecast data, attributing it to the use of the MAX function in selecting the highest monthly Peak HDD. It notes that the Monthly HDD has become more influential than Peak HDD in 2022 due to year-round residential heating impacts, leading to a steeper peak demand curve influenced by E3 electrification scenarios.

REMOVED) REDACTED 2022 Load Forecast Report Appendix D Page 9 of 9 Appendix D – Forecast Sensitivity Analysis Figure D8: Relative Impact of Inputs 2023 Energy 2023 Peak 2032 Energy 2032 Peak Item (GWh (MW) (GWh) (MW) Included in Forecast D...

AI summary The document presents a sensitivity analysis from the 2022 Load Forecast Report, highlighting the impact of various factors such as demand-side management (DSM), solar PV, electric vehicles (EV), and battery storage on energy and peak load forecasts for 2023 and 2032. It includes different scenarios for EV adoption and the effects of weather and economic factors.

2022 Load Forecast Report Appendix E Page 1 of 8 Nova Scotia Power Electrification Support Load Forecast Inputs – Overview April 2022 Liz Mettetal, PhD Sierra Spencer Michaela Levine Arne Olson Dan Aas REDACTED (CONFIDENTIAL INFORMATION RE...

AI summary This document is part of the 2022 Load Forecast Report Appendix E, prepared by Nova Scotia Power with contributions from E3, a consulting firm specializing in engineering, economics, and public policy. The report provides input for load forecasting related to electrification support.

EMOVED) 2022 Load Forecast Report Appendix E Page 5 of 8 Transportation Load Shaping Process

AI summary This section outlines the Transportation Load Shaping Process, which is part of the 2022 Load Forecast Report. It discusses strategies and methods for managing and shaping transportation-related electricity demand.

RMATION REMOVED) 2022 Load Forecast Report Appendix E Page 7 of 8 LDV Charging Profiles  Charging profiles represent population-level charging scaled down to one vehicle  In unmanaged charging, drivers begin charging immediately upon arr...

AI summary The document discusses LDV charging profiles, distinguishing between unmanaged and managed charging. Unmanaged charging occurs immediately upon arrival, while managed charging shifts timing to reduce costs and flatten peak loads. It also mentions the role of aggregators in managing EV charging and references a heating equipment stock rollover in the appendix.

ast given lack of data and reporting differences (e.g., primary heating source) – however, growth in HPs is aligned with NSP 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report Attachments 1-3 have been filed electronic...

AI summary The document mentions the filing and removal of attachments from the 2022 Load Forecast Report due to confidentiality concerns, indicating some information was redacted and not made publicly available.

to NSUARB IR-3 Attachment 1. 29 30 Date Filed: July 8, 2022 NSPI (CA) IR-2 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer...

AI summary NSPI responds to NSUARB's IR-3 request regarding geographic load data, stating billing data (1-2 months of consumption) is used for forecasting. Load flows are not evaluated for forecasting, and AMI data integration is under review for granular analysis. No geographic disaggregation by transmission zones is currently available.

29 coincident peak time of a weekday evening in January at hour ending 1800, so the 30 difference between the E3 models would be 0.6 kW/vehicle. Not all of the charging will Date Filed: July 8, 2022 NSPI (CA) IR-4 Page 1 of 2 REDACTED (CON...

AI summary NSPI acknowledges challenges in managing EV charging demand during peak hours, noting a 0.6 kW/vehicle difference in peak load scenarios. Temperature impacts EV efficiency and heating/cooling demands, though traffic data analysis for system peaks remains unreviewed. Only 70% of EVs are managed off-peak, with 30% remaining unmanaged.

(b) No. Both provincial and municipal governments have discussed targets related to 29 affordable housing and population growth, but no concrete policies or programs have been Date Filed: July 8, 2022 NSPI (CA) IR-8 Page 1 of 2 REDACTED (C...

AI summary The response addresses housing policy gaps, noting no concrete programs exist for affordable housing despite discussions. Energy load forecasting uses billing data for new homes, assuming higher efficiency. Population trends are modeled indirectly through economic variables like GDP and employment.

d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL

AI summary The document references NSPI's responses to Consumer Advocate information requests and the 2022 Load Forecast Report (NSUARB M10569), indicating regulatory proceedings involving demand forecasting and stakeholder engagement in Nova Scotia's energy sector.

Variable Unit Populated? Frequency Type Station pressure kPa Yes Hourly Objective Humidex N/A Sometimes Hourly Objective Windchill N/A Sometimes Hourly Objective Weather N/A Sometimes Hourly Subjective Max Temp Degrees Celsius Yes Daily Ob...

AI summary NS Power did not analyze certain factors in their load forecast, stating that temperature is the primary driver, and other factors may not be statistically significant when combined with temperature.

d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 Reference Report p. 86 Figure 60 “Weather-Normalized Firm Peak.” For items a-d bel...

AI summary The document outlines NSPI's responses to the Consumer Advocate's information requests regarding the 2022 Load Forecast Report, focusing on weather-normalized sales, peak load calculations, methodology documentation, and data for Figure 60. The request includes detailed workpapers and explanations for adjustments in load forecasting.

ciated with a 0.2 degree Celsius 23 variance in peak, assuming a weekday with full lighting load. 24 (v) What, if any, other factors impacted the 2019 variance? 25 Date Filed: July 2, 2020 NSPI (NSUARB) IR-13 Page 1 of 3 REDACTED (CONFIDEN...

AI summary The 2019 variance in peak load was influenced by differences in lighting load and the timing of the peak (morning vs. evening). The response highlights that temperature and behavioral patterns also contributed to the variance, making peak load prediction challenging.

1 Request IR-15: 2 3 According to Exhibit N-34, Matter No. M10431, Response to CA IR-41, Attachment 1, NS 4 Power has developed scaled class load shapes for 2019 using its load research sample. The 5 Report indicates that these data have b...

AI summary The request asks NS Power to provide updated load shapes, loss estimates, and explanations regarding the use of loss factors in their load forecasts. The response refers to confidential attachments and indicates that losses are calculated at the system level, not by class.

1 Request IR-17: 2 3 Respecting the Board’s direction to “evaluate improvements to the weather normalization 4 estimate and examine the impact of incremental cold on loads in the temperature ranges 5 where peak loads occur,” (Report, p. 12...

AI summary The Board directed NS Power to evaluate improvements to the weather normalization estimate and examine the impact of incremental cold on loads during peak temperature ranges. NS Power has not made an interim adjustment to its demand change metric, and the request asks for an explanation and a list of planned tasks to complete the Board's directed updates to the load forecast report.

Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to EfficiencyOne Information Requests NON-CONFIDENTIAL

AI summary The document references NSPI's responses to EfficiencyOne's information requests regarding the 2022 Load Forecast Report, part of NSUARB matter M10569.

ing Pricing (TVP), are being studied 28 through a pilot and will be compared against other managed charging approaches included 29 in the Smart Grid Nova Scotia pilot project. 30 Date Filed: July 8, 2022 NSPI (E1) IR-10 Page 1 of 2 10 - Ye...

AI summary NSPI is updating EV impact modeling to include E1's managed charging programs and the Smart Grid Nova Scotia pilot. E1's proposed demand response program for 2023-2025 is not incorporated into the 2022 Load Forecast, as it is assumed to be part of a broader IRP Action Plan demand response program.

1 Request IR-10: 2 3 With reference to Section 4.4 End-Use Intensity Trends, page 38 of 98, the application states 4 “the peak impact predicted by E3 using building stock modelling produces a higher peak 5 impact than the existing SAE peak...

AI summary The response explains that NS Power adjusted its SAE model to incorporate E3's more detailed building-level heat pump modeling, which accounts for reduced efficiency at peak temperatures and backup heat contributions. While energy differences were minimal, peak differences were larger, but NS Power did not adjust the forecast for heat pump sales.

sed on the end use survey of that year. As a result, the saturations 24 changed between the forecasts. The calculations can be found on the “HP” tab (columns 25 O and S) of 2022 Load Forecast Report Attachment 1 Residential Intensities. 26...

AI summary The document discusses the changes in heating and cooling intensities for heat pumps between the 2021 and 2022 forecasts. The heating intensity calibration value was adjusted to reflect higher winter loads, with a scaling factor increasing from 0.4 to 0.45. These changes are reflected in the 2022 Load Forecast Report Attachment 1 Residential Intensities.

ecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL

AI summary The document outlines NSPI's responses to information requests from the NSUARB regarding the 2022 Load Forecast Report. It is part of a regulatory proceeding and is marked as non-confidential.

86 23 21 18 10 18 2030 31 86 23 21 18 10 18 2031 31 86 23 21 18 10 18 2032 31 86 23 21 18 10 18 11 Date Filed: July 8, 2022 NSPI (NSUARB) IR-19 Page 1 of 1 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NS...

AI summary The document discusses NSPI's response to an information request regarding the use of price elasticity in load forecasting. NSPI explains that it is not aware of publicly available Canadian price elasticities specific to long-term electricity prices and refers to a previous response and an attachment for further details.

ergy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-31: 2 3 With reference to Section 6.3 Large General Service, page 70 of 98, the application in...

AI summary NSPI responded to an NSUARB information request regarding the 2022 Load Forecast Report, explaining that solar adoption is considered in the commercial class but not in the large general service class due to a lack of specific information on self-production.

2032 7 29,633.54 30,334.35 -700.803 0 2032 8 40,897.67 1 0 1 2032 8 29,657.96 30,358.76 -700.803 0 2032 9 40,930.56 1 0 1 2032 9 29,682.37 30,383.17 -700.803 0 2032 10 40,963.45 1 0 1 2032 10 29,706.79 30,407.59 -700.803 0 2022 LFR SBA IR-...

AI summary The text presents numerical data related to a regulatory proceeding, including figures for 2032 across multiple months, and references a 2022 Load Forecasting Report Standard Billing Adjustment Interim Report 1 Attachment 1 Page 6 of 6. It includes categories such as Residential, Small General Inputs, Small General Coefficients, and Small General Outputs.

Small General Inputs Small General Coefficients Small General Outputs Single Family Multi Family Completions Completions Residential Customer (Conference (Conference Customer accounts Board data) Board Data) Count Year Month SmlGenCustNMan...

AI summary The document contains data tables related to small general inputs, coefficients, and outputs, including customer account completions and energy demand forecasts. It references the 10-Year Energy and Demand Forecast from the 2022 Load Forecast Report (NSUARB M10569) and includes NSPI responses to information requests from the Small Business Advocate.

Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL

AI summary The document outlines NSPI's responses to information requests from the Small Business Advocate, based on the 2022 Load Forecast Report, as part of the NSUARB proceeding M10569.

drive growth 23 and remain competitive. This emphasis on clean growth, coupled with emerging 24 opportunities in areas such as critical minerals, electrification, low-carbon 25 construction materials and an array of clean technologies, wil...

AI summary The text discusses opportunities for clean growth in Canada, emphasizing areas such as critical minerals, electrification, and low-carbon construction materials. It highlights the importance of reducing industrial emissions and meeting demand for clean products. A 10-Year Energy and Demand Forecast from the Load Forecast Report is referenced, along with NSPI's responses to information requests.

to assist customers in minimizing upfront capital costs related to decarbonization & 20 electrification. 5 En4-460-2022-eng.pdf (publications.gc.ca) page 52 of 240 Date Filed: July 8, 2022 NSPI (SBA) IR-2 Page 3 of 3 10 - Year Energy and D...

AI summary The document discusses a request related to the 10-Year Energy and Demand Forecast, specifically regarding technologies assumed for space heating electrification and the separation of load data into electrification categories. NSPI provides a list of technologies considered, including various heat pump systems and electric heating solutions.

(SBA) IR-3 Page 1 of 4 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Electric Baseboards 2 Electric Ovens 3 Electric Infra...

AI summary The document provides a 10-year energy and demand forecast, highlighting growth in heating, transportation, and cooling. It notes that transportation growth is driven by EV adoption and increased cooling demand due to rising temperatures and longer summers. The forecast data is part of the 2022 Load Forecast Report (NSUARB M10569).

NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Federal Zero Emission Vehicle Sales Mandates 2 3 4 Assumption : Based on 50,000 yearly car sales in NS versus Federal government EV 5 adoptions targets 6 7 L...

AI summary NSPI provides responses to information requests from the Small Business Advocate, discussing industrial electric load growth driven by process equipment electrification and new industrial facility construction, noting minimal impact from transportation electrification based on IEA projections.

mand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 (c) The ELCC analysis completed for the 2020 IRP did not consider response time as an input 2 to th...

AI summary NSPI provided responses to information requests from the Small Business Advocate regarding the 2022 Load Forecast Report. It clarified that the BNI Curtailment values in the report were based on the 2019 DSM Potential Study, not the 2023-2025 DSM Resource Plan, and directed the requester to Appendix D of the 2019 study for detailed analysis.

d Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-9: 2 3 Please refer to Page 43, Lines 12-22. This section states that it is assumed 70% of...

AI summary The NSPI response to IR-9 discusses the management of EV charging, combining time-based rates and aggregator-based charge management. It clarifies that total kWh load remains unchanged between managed and unmanaged scenarios, but peak demand varies. The forecast assumes no impact on overall consumption, with annual load being the same across all cases.

d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Average Peak Average Peak Average Peak Vehicle Type Blended Unmanaged Managed (kW/vehicle) (kW/...

AI summary The document presents a demand forecast from the 2022 Load Forecast Report, focusing on vehicle types and their average peak load in both unmanaged and managed scenarios. NSPI notes no impact from the unmanaged scenario on other load types.

nd Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 Please refer to Figure 32 on Page 51. 4 5 (a) Please confirm that commercial heat...

AI summary NSPI confirms that commercial heat pump adoption is included in the 'Heat' and 'Cool' categories in Figure 32 but commercial electric vehicle adoption is not included in the commercial model. EV load is included in the residential model and will be reallocated to the commercial class in the 2023 forecast, with increasing impacts from 2025 to 2032.

d Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-17: 2 3 Please refer to Page 19, Lines 9-20. 4 5 (a) Why does the weather data start year (...

AI summary NSPI explains that weather data starts in 2012 for residential, small general, and general demand models, aligning with those models' timeframes. Small and medium industrial forecasts are econometric and not weather-sensitive. The peak demand forecast uses a 2012–2021 timeframe, consistent with other models, and the use of longer data series for industrial energy forecasts does not affect peak forecasts.

nd Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-19: 2 3 Please refer to Electric Vehicles (EVs), page 42 of 98, Lines 2-11, which states:...

AI summary The document discusses Nova Scotia Power Inc.'s (NSPI) response to a request regarding the 2022 Load Forecast Report, focusing on electric vehicle (EV) adoption forecasts, including targets set by provincial and federal policies, and questions about the validity and adjustments made to the forecast model.

239.8 2032 9,871.6 482,771.0 627.7 12,297.0 923.6 18,468.3 513,536.4 16000 4860 0.983084964 286.5 5,047.4 (3.5) 510 (176) 330.2 5,377.5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 LFR Synapse IR-33 Attachment 1 Page 4 of 4 Residential...

AI summary The document presents residential load forecasts for 2022 and 2032, including impacts from demand-side management (DSM) programs. It shows projected changes in residential electricity use, EV adoption, solar penetration, and DSM-driven reductions in sales. The data highlights a 6.6% increase in residential sales with DSM by 2032.

to NSUARB IR-3 Attachment 1. 29 30 Date Filed: July 8, 2022 NSPI (CA) IR-2 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer...

AI summary NSPI responds to NSUARB's request regarding geographic load data, stating that load flows are not used in forecasting, billing data introduces uncertainty if disaggregated, and AMI data integration is under review for granular analysis.

1 Request IR-4: 2 3 Reference Report pp. 43-44: “The management of charging in this scenario was based on 4 minimizing the cost of electricity to charge with the electric rates referenced being the 5 existing time of use tariffs that are c...

AI summary Request IR-4 seeks clarification on NS Power's assumptions about managed EV charging demand reductions, weather impacts on EV loads, and traffic data reviews. NS Power responds by contrasting previous unmanaged charging estimates with E3's models but does not directly address weather or traffic data impacts.

56.5 36.2 27.6 37.9 21.5 2032 73.2 55.0 35.7 26.9 37.5 20.9 3 Date Filed: July 8, 2022 NSPI (CA) IR-5 Page 2 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569)...

AI summary NSPI confirms that the 'DSM captured by end uses' column in the 2022 Load Forecast Report is calculated by subtracting the 'Res DSM Adjustment' from the 'Total Res DSM' column. The forecast uses a regression model incorporating past DSM activity, price, appliance efficiency, and economic variables. NS Power lacks historical appliance-level DSM data, relying instead on class-level annual savings.

nd Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL Model Statistics Mean Abs. % Err. (MAPE) 2.69% Durbin-Watson Statistic 1.831 Durbin-H Statistic #NA Lj...

AI summary The document discusses statistical metrics of a 10-year energy and demand forecast model (NSUARB M10569), noting a 2.69% MAPE and consistent DSM savings (27 MW/year). NSPI asserts that DSM alignment between energy and peak models ensures the model's validity, citing historical consistency and alignment with energy DSM savings.

Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 Reference Report p. 59: “The non-weather variance in 2021 is mainly related to an inc...

AI summary NSPI responds to a consumer advocate's inquiry about factors influencing demand forecasts, noting population growth since 2016, no pandemic-driven migration analysis, and lack of concrete housing policies. The response addresses residential and commercial model assumptions, efficiency in new construction, and forecasted customer growth.

cover, precipitation and wind speed. 28 (g) Please provide NS Power’s best estimate (quantitative or qualitative) as to the impact 29 of cloud cover, snow cover, precipitation, and wind speed on loads, including peak 30 loads. Date Filed:...

AI summary NS Power responds to a request about weather variables affecting load, referencing a 10-Year Energy and Demand Forecast. They provide a temperature-load correlation (R²=0.8556) and list objective variables from Environment Canada, with precipitation sometimes populated.

d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL

AI summary The document references a 2022 Load Forecast Report and NSPI's responses to Consumer Advocate information requests. It is marked as non-confidential and associated with NSUARB matter M10569, indicating regulatory proceedings related to demand forecasting and stakeholder information disclosure.

Variable Unit Populated? Frequency Type Wind speed km/h Yes Hourly Objective Visibility km Sometimes Hourly Objective Station pressure kPa Yes Hourly Objective Humidex N/A Sometimes Hourly Objective Windchill N/A Sometimes Hourly Objective...

AI summary The text presents a table of weather variables (e.g., wind speed, temperature, precipitation) with metadata. It also notes that NS Power did not analyze certain load forecast factors, emphasizing temperature as the primary driver of load impacts.

Date Daily Avg Temp Daily Load 10/11/2021 14.02083 25914.76 10/12/2021 15.75417 26505.84 10/13/2021 14.37917 26463.92 10/14/2021 15.03333 26970.68 10/15/2021 12.23333 26588.97 10/16/2021 12.8625 26187.48 10/17/2021 16.49167 26604.07 10/18/...

AI summary The document presents a table showing daily average temperatures and corresponding daily load values from October 11 to December 11, 2021. The data reflects the relationship between temperature and electricity demand over this period.

t (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL

AI summary The document outlines NSPI's responses to information requests from the Consumer Advocate, based on the 2022 Load Forecast Report, and is related to the NSUARB matter M10569.

August-21 August Normal Total Load from Load Load Load Load Heating Actual Actual Month Load From Load From Load From Load from Load from Total Heating Actual from Feb From From Load Load from Load Day Actual HDD 0 HDD 13 CDD18 HDD13 HDD 1...

AI summary The text provides a table with load data, including HDD (Heating Degree Days) and CDD (Cooling Degree Days) values, along with load measurements in MWh. The data appears to track energy consumption patterns over a specific period, likely for regulatory or planning purposes.

ciated with a 0.2 degree Celsius 23 variance in peak, assuming a weekday with full lighting load. 24 (v) What, if any, other factors impacted the 2019 variance? 25 Date Filed: July 2, 2020 NSPI (NSUARB) IR-13 Page 1 of 3 REDACTED (CONFIDEN...

AI summary The 2019 variance in energy demand was primarily influenced by differences in lighting load and the timing of the peak (morning vs. evening). Variance at specific temperatures can reach up to 200 MW, driven by individual behaviors, temperature, and weather patterns.

1 Request IR-15: 2 3 According to Exhibit N-34, Matter No. M10431, Response to CA IR-41, Attachment 1, NS 4 Power has developed scaled class load shapes for 2019 using its load research sample. The 5 Report indicates that these data have b...

AI summary The request asks NS Power to provide updated scaled class load shapes, estimates of monthly losses by class, and an explanation for using 2013 loss factors instead of more recent data. The response refers to confidential attachments and explains that losses are calculated at the system level, not by class.

1 Request IR-17: 2 3 Respecting the Board’s direction to “evaluate improvements to the weather normalization 4 estimate and examine the impact of incremental cold on loads in the temperature ranges 5 where peak loads occur,” (Report, p. 12...

AI summary The request asks NS Power to explain why it has not made an interim adjustment to its demand change metric following the Board's direction, referencing Wilson testimony from M10109. It also requests a list of tasks for updating the load forecast report. The response indicates that NS Power agreed with the Board's direction to re-evaluate weather normalization and peak load forecasting methods.

rovince’s efforts to reduce future carbon 14 emissions. 2 My evidence will discuss these and several other revisions. 15 III. Directives from the 2021 Load Forecast Report Proceeding 16 Q: Please summarize NS Power’s actions in response to...

AI summary NS Power updated its load forecasting methods per 2021 Board directives, including revised peak design temperatures and warming trend incorporation. However, it deferred analyzing incremental cold's impact on peak loads, planning to address this in 2023. John D. Wilson testified about these revisions and recommended including wind speed analysis.

ecast Report, pp. 19-24. 7 Exhibit N-7, NS Power response to CA IR-1(c-d). 8 Each of the major components is multiplied by a regression factor, which is close to 1.0 for heating and other, but only 0.6 for cooling. 9 Exhibit N-7, NS Power...

AI summary The analysis examines cooling and heating model outputs, showing increasing CDD-driven cooling demand but stable heating demand despite decreasing HDD. Heat pumps and electrification are suggested to offset reduced heating needs, though NS Power requires further validation of electrification load forecasts.

ve formulations of 3 the warming trend. NS Power should continue to consider potential improvements to its 4 methods for addressing climate change scenarios in its load forecast.

AI summary The text recommends that NS Power should continue refining its methods for addressing climate change scenarios in load forecasting, emphasizing the need to account for warming trends in future planning.

predictive of load levels. 18 10 Mr. Wilson also recommended that the Board should require discussion of other weather 11 measures, including wind speed and cloud cover. 19 12 Q: Has NS Power responded to his recommendation on multi-hour a...

AI summary The text discusses recommendations to use multi-hour average temperatures for weather normalization and peak load forecasting, with NS Power noting a 2021 weather-normalized peak discrepancy. It also highlights that population distribution is not a reliable proxy for hourly load distribution, citing Exhibit N-8 from Matter No. M10109.

e load carrying capability (ELCC) of wind power resources. It may be appropriate to recognize that daily average wind speed is correlated with both load and generation in that calculation. Evidence of John D. Wilson • Matter No. M10569 • J...

AI summary The text discusses the impact of wind speed on load carrying capability (ELCC) of wind power resources, noting that higher wind speeds correlate with increased load during cold days. John D. Wilson's evidence suggests incorporating wind speed into load forecasts could improve NS Power's weather normalization and forecast accuracy for operational planning.

testimony with Paul Chernick in Nova Scotia Power’s application for the Advanced Distribution Management System Upgrade on behalf of the Nova Scotia Consumer Advocate. Need for the ADMS and integration with the Distributed Energy Resources...

AI summary Paul Chernick provided testimony in multiple regulatory proceedings, including Nova Scotia Power’s ADMS Upgrade, 2020 Load Forecast, and San Diego Gas & Electric’s EV Charging Program. His testimony focused on ensuring equitable and effective program implementation, budget controls, and evaluation processes.

30% -0.58% Industrial 25% +2.44% Total 100% +3.44% Source: Synapse from NSPI load forecast report. In general, the forecast seems reasonable, but there are significant increases in the energy and peak requirements from the previous forecas...

AI summary The NSPI 2022 load forecast shows significant increases in energy and peak requirements, driven by electrification and growth. Synapse recommends exploring DSM program impacts, stakeholder engagement, and technologies like battery storage to address forecast uncertainties and improve accuracy.

stomers increased by 1.3 percent over the forecast period. New customers increased the total residential load by 4.9 percent.3 The choice of drivers seems reasonable but should be reviewed every year. The residential statistical model incl...

AI summary The 2022 Load Forecast Report discusses residential load increases due to new customers and the impact of a COVID-19 binary variable adjusted over time. The model's choice of economic indicators for commercial and industrial sectors is deemed reasonable, though uncertainties in economic forecasts are noted. The methodology for load forecasting and DSM effects are highlighted as areas requiring ongoing review.

trends. The primary change drivers for XOther are water heat (increased electric heater saturation), reductions in lighting use, and miscellaneous. The net effect is to increase XOther by 1.5 percent. From this one can see that there are m...

AI summary The document analyzes residential energy use factors, noting heating (42%), cooling (2%), and other uses (56%) drive average consumption. Forecasts show slight increases from XHeat (-0.4%), XCool (+1.6%), and XOther (+0.9%), with NSPI applying adjustments for new customers, EVs, solar, RTR markets, and DSM savings. Appendix B provides regression model results and adjustments.

rom Page 5 of Appendix B. The first column shows the SAE regression model results, and the other columns reflect various adjustments to the forecast. 9 Load Forecast Report, Appendix B, pp.6-7. Synapse Energy Economics, Inc. Evidence Regar...

AI summary The document presents a residential load forecast analysis using a SAE regression model, adjusted for factors like EV adoption, solar energy, and demand-side management (DSM). It quantifies load changes from 2022 to 2032, showing increased residential demand and the impact of DSM programs on energy consumption.

-3.1% -0.1% -6.8% 6.6% load Note: Res Sales = Existing Customer Load + New Customer Load + EV Load + Solar Load + RTR + DSM. Source: NSPI load forecast report Appendix B. Heat pumps The heat pump section of the report discusses replacement...

AI summary The report forecasts heat pump saturation increasing from 35% (2022) to 66% (2032), with residential load changes offsetting due to fossil-to-electric heating replacements. Cooling demand (XCool) rises 78%, but overall residential load increases only 1.3% due to heating efficiency gains. Uncertainty remains about installation modes (sole heat source vs. hybrid systems) and actual saturation rates.

r clarification is the mode of these new heat pump installations. For example, whether the new installation is the sole heating source, or whether some existing fossil heating systems remain in place. There is some uncertainty as to the ov...

AI summary The text discusses uncertainties around the peak load effects of heat pump installations and the conversion of water heaters to electric models. It recommends further investigation into these impacts and monitoring of energy usage trends. NSPI is asked to provide updates on demand response initiatives for water heaters.

PI provide updates on the water heating load control project in the next load forecast report, including estimates of the impact of hot water heater device control initiatives on system peak demand.13 Electric vehicles Electric vehicles re...

AI summary The document discusses updates on water heating load control and electric vehicle (EV) load growth, noting EVs could contribute 12.5% of vehicle stock by 2032, with energy load estimates of 510 GWh and peak impacts of 89-131 MW. Uncertainty surrounds EV adoption due to supply chain issues and public goals. NSPI's SGNS project tests utility control of EV charging to shift demand to off-peak times, with a request for more SGNS results in future load forecasts.

harging, to shift electric vehicle charging to off-peak times.16 We ask that more complete results of the SGNS project regarding electric vehicle impacts be included in the next load forecast report. Solar generation (PV) Solar generation...

AI summary The text discusses load forecasting considerations for electric vehicles, solar PV, and battery storage, noting their potential impacts. It requests more comprehensive SGNS project data on EV and battery storage impacts, and highlights new customer contributions to residential load growth.

a reduction of 2 GWH in 2022 to 24 GWH in 2032. For the medium general load, it goes from 17 to 187 GWh, or 7.9 percent of the load in 2032. No explicit adjustments are indicated for other customers. The adjustments discussed in the foreca...

AI summary The forecast discusses load adjustments, noting a significant increase in system peak and the need to adjust DSM savings factors. Adjustments are deemed reasonable but with statistical uncertainties. Increased DSM savings may require upward adjustments.

he peak is first modeled statistically using historical data and economic and demographic projections to produce a Modeled Peak, and then NSPI applies various adjustments to arrive at the System Peak. Table 5. Peak contribution components...

AI summary NSPI models peak demand using historical data and adjustments, with commercial/industrial electrification as the largest growth driver. The 2032 System Peak increases by 350 MW, driven by electrification, residential heating, and EV adoption, though demand response could mitigate some impacts.

peak shares are shown in Figure 61 for the residential sector and in Figure 62 for the commercial sector. Our understanding is that these contributions are in the Modeled Peak values shown previously. In the NSPI response to E1 IR-9, it wa...

AI summary The text requests NSPI to clarify heat pump performance during peak loads, quantify ETS's role in reducing peak demand, and investigate water heating load control. It notes a shift from resistance heating to heat pumps in residential heating but highlights increased water heating contributions. Induction cooking's potential impact on energy use is also mentioned.

f induction cooking is more efficient than current stoves and its possible effects considered. Induction cooking is a new technology that should be evaluated for its effects on energy and peak loads. For the commercial sector, the heat end...

AI summary The text requests NSPI to evaluate commercial heat use impacts on peak loads and refine peak forecasting methods, citing discrepancies between forecasts and actual data. Sensitivity analyses highlight weather and economic factors as key uncertainties, with ongoing efforts to improve forecasting using class-specific and AMI data.

be biased by outliers; • Testing if Median Household Income provides a more accurate indicator of the level of income in the province is encouraged; and • Consider incorporating household size and age of household residents to determine if...

AI summary Synapse Energy Economics requests NSPI to enhance its 2022 load forecast by investigating heat pump effects, electric vehicle impacts, battery storage, and commercial electrification programs. Recommendations include incorporating household demographics, improving data on load control projects, and evaluating program cost-benefit analyses.

• We also raise a point about the appropriateness of the commercial electrification programs. We ask NSPI to provide further information about their relative benefits and costs (p.14). • It is not clear in the report how much of the commer...

AI summary The text outlines requests for clarification and further analysis from Synapse Energy Economics, Inc. regarding NSPI's 2022 load forecast, focusing on commercial electrification programs, demand savings, EV impacts, time-of-use rates, DR measures, thermal storage, and emerging technologies like induction cooking. Questions emphasize cost-benefit evaluation, sector-specific DSM effects, and load management strategies.

effects on energy and peak loads (p.20). • We ask NSPI to evaluate commercial heat use more fully as to what is driving it and how the peak impacts could be moderated (p.20). • We ask that NSPI review its peak forecasting methodology in li...

AI summary The text requests NSPI to improve peak load forecasting by evaluating commercial heat use, reviewing methodology, and conducting sensitivity analyses. It also supports NSPI's efforts to enhance forecast transparency. Synapse Energy Economics, Inc. provided evidence on the 2022 load forecast.

EfficiencyOne Evidence 1 climate, including but not limited to, Efficiency Vermont, Massachusetts Clean Energy Center, 2 National Grid, Efficiency PEI, and Energy Star. 3 4 The NS Power On-Bill Financing Study Update (M09321) dated Decembe...

AI summary EfficiencyOne (E1) argues that NS Power's 2022 Load Forecast overestimates peak demand by relying on electric resistance heating below -7°C, potentially misrepresenting cold climate heat pump adoption. E1 recommends updating the model to reflect higher COP standards (1.75 at -15°C) and removing lock-out temperatures. NS Power's assumptions about heat pump adoption rates may not align with market-driven trends.

systems. 16 In its response to E1 IR-05 (b), when asked to confirm if these changes are expected 26 to be market-driven, NS Power stated, “[t]he model is not based on economic uptake. It is based 15 M09321, P701, On-Bill Financing Study Up...

AI summary NS Power's load forecast model prioritizes 100% heat pump saturation to meet emission targets, but lacks evidence for this approach. Alternative heating systems (e.g., ETS, wood stoves) could achieve similar policy goals with different load impacts and market uptake potential, challenging NS Power's assumptions.

recast incorporates the following demand response programs: 25 • Large Industrial Interruptible Rider (LIIR); 26 • Extra Large Industrial Active Demand Control (ELIADC) tariff; 23 M10569. NS Power 2022 Load Forecast Report, page 80 of 98,...

AI summary Recast integrates demand response programs like LIIR, ELIADC, and EV managed charging into load forecasts, removing their capacity value from firm load and applying an ELCC factor. NS Power explains ELCC factors apply when DR resources are constrained in duration or call frequency.

consumption patterns in response to changes in the price of electricity over time, or to incentive 24 payments designed to induce lower electricity use at times of high wholesale market prices or 28 M10569. NSPI (E1) RIR-7, page 3 of 3, li...

AI summary EfficiencyOne (E1) recommends that Nova Scotia Power (NS Power) apply a consistent framework to evaluate the value of demand response programs, particularly in assessing consumption patterns influenced by electricity pricing and incentive payments during high wholesale market periods or system reliability risks.

22 Load Forecast Report seems reasonable and points out that 25 the forecast shows significant increases in energy and peak related to electrification and population 26 and economic growth. 3 27 1 Nova Scotia Wholesale and Renewable to Ret...

AI summary The rebuttal of the 2022 Load Forecast Report is deemed reasonable, citing significant increases in energy and peak demand due to electrification, population growth, and economic expansion. It references the Nova Scotia Wholesale and Renewable to Retail Electricity Market Rules and associated legal documents.

understanding of demand on the load by time of day is 32 achieved. 33 34 We found NSPI to be responsive to these recommendations and are attaching 35 those responses to Synapse IR-43 to this report. We also note that some changes 36 are be...

AI summary The document discusses NSPI's responsiveness to recommendations, with some changes delayed until 2023. It references a rebuttal evidence report and cites Synapse IR-43 as part of the 2022 Load Forecast Report process.

lude 24 them in the 2023 Load Forecast. 25 26 For clarification, weather normalization refers to an analysis of the variance between forecast and 27 actual values, but it is not an input to the forecast. 28 5 M09321, Exhibit N-9, NS Power...

AI summary The text clarifies that weather normalization analyzes variance between forecast and actual values but is not an input to the 2023 Load Forecast. It references NS Power's On-Bill Financing Report (Exhibit N-9) from October 30, 2020, filed in Matter M09321.

, 2022 Page 9 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 3.1.5 Recommendation 5: 2 3 “We ask that more complete results of the SGNS project regarding 4 battery storage be included in the next load forecast report. We 5 fur...

AI summary The document includes two recommendations and NS Power's responses. Recommendation 5 requests inclusion of SGNS project battery storage results and EV battery peak management analysis. NS Power states data collection is ongoing, with updates reported to NSUARB. Recommendation 6 questions commercial electrification programs' appropriateness; NS Power clarifies no specific programs exist, referring customers to third-party options.

32 rates and other measures to mitigate the peak load increases for 33 all these components, especially for the commercial and industrial 34 sectors”. DATE FILED: September 26, 2022 Page 12 of 25 2022 Load Forecast Report Rebuttal NON-CONF...

AI summary The text emphasizes the need for rates and other measures to address peak load increases, with a focus on commercial and industrial sectors. It is part of a 2022 Load Forecast Report Rebuttal submitted on September 26, 2022.

Response: 29 30 NS Power confirms that at present, electric thermal storage is not widely used as 31 either a primary or backup heat source, with only 13,000 customers using the DATE FILED: September 26, 2022 Page 13 of 25 2022 Load Foreca...

AI summary NS Power responds to recommendations regarding load management technologies, agreeing with some but noting limitations. It acknowledges low adoption of electric thermal storage, potential for water heating load control, and the long-term impact of induction cooking. Commercial heat use analysis is also recommended for future forecasts.

.1.16 Recommendation 16: 30 31 “We ask NSPI to evaluate commercial heat use more fully as to 32 what is driving it and how the peak impacts could be moderated.” 33 DATE FILED: September 26, 2022 Page 14 of 25 2022 Load Forecast Report Rebu...

AI summary NS Power responds to recommendations regarding commercial heat use forecasting, peak load discrepancies, and 2032 peak sensitivity analysis. It attributes peak differences to weather adjustments, validates methodology using 2022 data, and cites collaboration with E1 and alignment with the IRP Action Plan.

2022 Page 16 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 mitigation strategies, intended to provide the necessary context to assess the 2 current forecast with consideration for changes in policy/technology. 3 4 3.2 Consume...

AI summary The Consumer Advocate (CA) recommends that NS Power analyze wind speed's impact on peak loads in its 2023 forecast and focus weather analysis on peak load. NS Power agrees, committing to include multi-hour temperature averages and wind speed analysis in the 2023 Load Forecast Report.

outlined in Section 2, refining the impact of electrification on the load 29 forecast will be undertaken in future forecasts through the inclusion of new 30 learnings and data. 31 DATE FILED: September 26, 2022 Page 18 of 25 2022 Load Fore...

AI summary The text references refining the impact of electrification on load forecasts through future forecasts incorporating new data and learnings. It is part of the '2022 Load Forecast Report Rebuttal' filed on September 26, 2022, as a non-confidential document.

26, 2022 Page 22 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL

AI summary This document is a rebuttal to the 2022 Load Forecast Report, submitted as part of a regulatory proceeding. It does not provide detailed content but indicates the existence of a formal response to a load forecast analysis.

1 3.4.3 Recommendation 3: 2 3 “That NS Power provide a plan and explanation for the 4 remaining demand response capacity required to satisfy the 5 projected 2025 capacity, detailing the incremental program 6 options to be utilized and how...

AI summary The document discusses NS Power's response to recommendations regarding demand response (DR) capacity planning and the application of an ELCC factor to DR programs. NS Power explains that DR potential was identified using E1’s study and that DR is included in the load forecast as a foreseeable opportunity. The company is consulting with E1 and stakeholders on DR implementation.

the load forecast. 29 30 NS Power Response: 31 32 E1 states that “[LIIR] the ELCC is likely to be very close to 1 but it should still be 33 considered given the program does not represent perfect capacity”. 14 This is 14 M10569, Exhibit N-...

AI summary NS Power responds to E1's claim regarding the ELCC for LIIR customers, arguing that a quantitative ELCC analysis is unnecessary given both parties agree the value is close to 1. NS Power acknowledges EV charging characteristics similar to ELCC adjustments and has modeled 70% of EV load as responsive in the 2022 Load Forecast.

22 Page 24 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 4.0 CONCLUSION 2 3 The requirement to file the Load Forecast Report is an annual requirement under the provisions of 4 the Nova Scotia Wholesale and Renewable to Retail...

AI summary NS Power has submitted the 2022 Load Forecast Report, highlighting continuous improvements in transparency and accuracy with input from Synapse. The report reflects ongoing efforts to refine forecasting methodologies and explain underlying factors. NS Power requests the Board's acceptance of the report.

the Board accept the 2022 Load Forecast Report, as filed. 15 M10569, Exhibit N-9, Synapse Evidence, July 29, 2022, page 1. 16 Ibid, page 24. DATE FILED: September 26, 2022 Page 25 of 25

AI summary The document discusses the acceptance of the 2022 Load Forecast Report by the Board, referencing specific exhibits and pages from Synapse Evidence dated July 29, 2022.

HEARING ORDER M10569 NOVA SCOTIA UTILITY AND REVIEW BOARD IN THE MATTER OF the PUBLIC UTILITIES ACT -and- IN THE MATTER OF NOVA SCOTIA POWER INCORPORATED’S 2022 Load Forecast Report BEFORE: (^fklchard Melanson, LL.B., Member (^Julia Clark,...

AI summary The Nova Scotia Utility and Review Board outlines a procedural timetable for reviewing Nova Scotia Power Inc.'s 2022 Load Forecast Report under the Public Utilities Act. The process includes intervention notices, information requests, evidence submissions, and adherence to regulatory rules, with a paper hearing format.

-0.6% 2018 0.8% 3.5% 6.0% 2.7% 2017 1.0% 0.6% -0.03% -4.4% The load forecast is a foundational input in relation to NS Power’s overall planning, budgeting, and operating activities, including generation requirements, capital program, fuel...

AI summary The document emphasizes the critical role of load forecasting in NS Power's planning and operations, noting the Board's past concerns about forecast accuracy. NS Power revised the 2022 Load Forecast by incorporating warming trends and EV adoption models, with 2021 variances attributed to weather (184 GWh) and the pandemic (63 GWh). The Board directed improvements in forecasting methodology.

n process, as well as a scenario analysis for ETS to moderate peak load. It will monitor growth in heating load for commercial customers and work with EOne on possible firm peak mitigation strategies. NS Power confirmed that the model used...

AI summary NS Power discusses heat pump COP assumptions, DSM allocation between commercial and industrial classes, and rejects intervenor requests on line loss modeling and ELCC factors for LIIR. It also denies a request for commercial electrification analysis and plans to address EV load shapes with ELCC adjustments.

by 2031, and the previous 27 forecast of 58,000 EVs in the 2021 Load Forecast. Please provide a table with the data that was 28 used in each model. 29 30 Request IR-17: 31 With reference to Section 4.4 Electric Vehicles, page 43 of 98, the...

AI summary The text requests clarification on discrepancies in electric vehicle (EV) forecast data between 2021 and 2031, asks for detailed data tables used in models, and inquires about the data source (Nova Scotia, Canadian, American, or other) for E3’s EV simulation tool.

Document #:296069 Date Filed: June 14, 2022 Synapse (NSPI) Page 4 of 16 1 c. Please provide supporting evidence for the 35 percent saturation of customers providing 2 their heat via heat pumps in 2022 (p.36). 3 d. Please provide the data s...

AI summary The document outlines regulatory requests from the Board to NSPI and E3 regarding heat pump saturation data, residential water heater efficiency standards, and electric vehicle load patterns. Requests focus on evidence, data sources, and impact analyses for heat pumps, water heating technologies, and EV integration into the grid.

eliminary results from the SGNS project that are available, especially EV 34 charging patterns. 35 e. Please provide the inputs and the calculations behind the results in Figure 28.

AI summary The text requests preliminary results from the SGNS project, focusing on EV charging patterns, and asks for the inputs and calculations behind Figure 28 to ensure transparency and accuracy in the analysis.

each load component element (residential, 17 small general service, general service, large general service, small industrial, medium 18 industrial, large industrial, municipal, losses, own use,) to the total NSR as shown in Figure 19 52 fo...

AI summary The text outlines requests for detailed information on load components, demand response (DR) resources, energy loss cost correction (ELCC) calculations, DR savings, peak demand modeling, and data calibration. Specific figures (53, 54, 58, 59) and unexplained values are highlighted for clarification.

v) NS Power’s rebuttal was agreeable with many of the recommendations by intervenors. 34 However, in response to intervenors’ request for more information on the Smart Grid Nova Document #:296069 Date Filed: June 14, 2022 Synapse (NSPI) Pa...

AI summary NS Power agreed with intervenors' recommendations but noted preliminary results from Smart Grid and Water Heating Demand Response projects. The Board directed NS Power to report these projects' load impact in the 2022 Load Forecast.

Page 1 of 5 1 Request IR-1: Please provide all inputs and outputs to the customer count models for all classes 2 relying upon such a model in excel format like those provided in Attachments 5-9. 3 4 Request IR-2: Please refer to page 52 of...

AI summary The document contains five requests (IR-1 to IR-5) seeking data on customer count models, electrification program targets, load forecasting assumptions, system peak growth rationale, and EV adoption forecasts. Requests focus on transparency around electrification strategies, program incentives, and load modeling methodologies.

M10569 – SBA IRs – June 16, 2022 Page 2 of 5 1 Request IR-7: Please refer to Figure 54: Demand Response on page 81 of the Filing. 2 a) Do the values represented in the Business, Non-Profit & Industrial (BNI) Curtailment 3 column correspond...

AI summary The SBA requests clarification on BNI curtailment data alignment with prior analyses, third-party EV charging aggregator identification, and assumptions behind managed/unmanaged EV load scenarios. Questions focus on methodology, stakeholder roles, and impacts on system reliability and load profiles.

t available.” 42 43 a. Please describe and provide any geographic disaggregation of load data that NS Power 44 has available, such as by transmission zone or substations. 45

AI summary The document includes a request for geographic disaggregation of load data by NS Power, specifically by transmission zones or substations.

Date Filed: June 16, 2022 CA (NSPI) Page 2 of 11 1 b. If NS Power does not have load data by transmission zone, please explain how NS 2 Power determines load flows in transmission planning. 3 4 c. Please discuss NS Power’s plan to develop...

AI summary The document includes regulatory requests to NS Power regarding load data management, transmission planning, EV charging impact assumptions, and DSM adjustments. Questions focus on data collection methods, EV load modeling, and demand-side management efficacy, with specific references to technical reports and forecasting methodologies.

Date Filed: June 16, 2022 CA (NSPI) Page 7 of 11 1 difference is attributed to March 2 not being “a particularly cold day” on which the temperature 2 dropped rapidly to the daily minimum. 3 4 a. Please confirm or correct our understanding...

AI summary The text includes requests for clarification on load forecast methodologies, specifically the use of a 25 MW/°C demand change estimate, its exclusion of weekend data, and the Board’s direction to improve weather normalization. Questions also seek explanations on NS Power’s lack of interim adjustments and evidence supporting temperature measurement approaches.

er plans to complete the Board directed 30 updates to the load forecast report; for each task, please provide (i) the estimated time 31 to complete the task, (ii) identify any prerequisite tasks, and (iii) identify whether the 32 task is n...

AI summary The Board directed updates to the load forecast report, requesting task timelines, prerequisites, and necessity. Request IR-18 asks to confirm a temperature trend in the forecast and explain its reflection in Table A1, including HDD and CDD values by year.

Date Filed: June 16, 2022 CA (NSPI) Page 9 of 11 1 d. Please provide a list of tasks that NS Power would need to complete to utilize the LRS 2 data in all applicable aspects of the load forecast; for each task, please provide (i) the 3 est...

AI summary The text outlines requests for clarifications on load forecasting methodologies, including DSM scenario analysis, temperature data usage in models, and factors influencing binary adders in monthly load forecasts. It emphasizes the need for NS Power to detail tasks for integrating LRS data and address discrepancies in figure references.

1 (a) Is the load forecast intended to estimate how much demand response (DR) capacity 2 is available during the system peak (highest single hourly average demand in a year)? 3 If no, please explain the reason for such limitations. 4 (b) E...

AI summary The text raises questions about NS Power's load forecast methodology, including DR capacity estimation, ELCC factor application, and discrepancies between NS Power and E3 models regarding commercial electric heating projections. It challenges whether NS Power's 48% ELCC underrepresents DR value and if ELCC should apply to other time-limited resources.

wer’s model estimates commercial electric heating share in 2021 24 to be ~52%, while E3’s model estimates the share to be ~27%. This is a significant variance, 25 particularly in the near-term. Date Filed: June 16, 2022 E1 (NS Power) Page...

AI summary EfficiencyOne (E1) highlights a significant variance between its model (~52%) and E3’s model (~27%) in estimating commercial electric heating share in 2021. This discrepancy is emphasized as a key issue in the 2022 Load Forecast Report proceeding (M10569).

Requests to Nova Scotia Power Inc. (NS Power) In the Matter of Nova Scotia Power Incorporated’s 2022 Load Forecast Report – M10569 NON-CONFIDENTIAL 1 (a) Please describe the specific assumptions and/or modelling approaches that are 2 causi...

AI summary The document requests Nova Scotia Power Inc. (NS Power) to clarify assumptions in its 2022 Load Forecast Report, including heat pump performance modeling, commercial electric heating share estimates, model calibration issues, and low-temperature COP behavior. Specific focus areas include heat pump capacity decline, backup heating reliance, and temperature thresholds for COP performance.

1 (b) Please elaborate on any coincidence factor associated with the electric backup of 2 heat pumps. Is any coincidence factor assumed for electric backup heating in the 3 RESHAPE analysis? 4 (c) What penetration of electric resistance he...

AI summary The text includes questions about heat pump backup systems, RESHAPE analysis assumptions, climate change impacts on heating/cooling degree days, low-GWP refrigerants, and EV charging management in Nova Scotia. NS Power is asked to elaborate on technical and modeling considerations.

ation measures that NS Power is using for the quoted 25 comparison above? 26 b) What decarbonization measures are the electrification measures being compared 27 to? Date Filed: June 16, 2022 E1 (NS Power) Page 7 of 8 EfficiencyOne (E1) Inf...

AI summary EfficiencyOne (E1) is inquiring about Nova Scotia Power Inc.'s (NS Power) 2022 Load Forecast Report, specifically seeking details on electrification and decarbonization measures, their cost-benefit analyses, and the comparison methodology used in the report.

cle-to-Grid and managed charging discussed 1 Exhibit N-1, 2022 Load Forecast Report, Page 44 T: 902-835-8544 F: 902-835-4310 E: [email protected] www.blackburnlaw.ca SUITE 231 BEDFORD HOUSE, SUNNYSIDE MALL, 1595 BEDFORD HIGHWAY, BEDFORD...

AI summary The SBA criticizes NSPI's Load Forecast Report for insufficiently explaining how SONS Project data influenced EV load and peak contributions. It also argues electrification forecasts rely on emissions targets rather than current incentives, risking overestimation of 605 GWh load and 267 MW peak demand by 2030.

EV forecast, investigation of new technologies to reduce energy and peak demand, and the management of peak through the Smart Grid Nova Scotia (SGNS) project results and direct control water heaters. The CA filed evidence prepared by John...

AI summary The document discusses John Wilson's recommendations for NS Power to improve climate change scenario analysis, enhance weather station integration, refine peak load forecasting models, and address modeling errors in residential energy models. Wilson also suggests adjustments to heat pump assumptions and EV usage during peak periods, alongside completing the line loss determination model with quarterly reporting.

ing storm closures at peak periods. Lastly, Mr. Wilson requested that NS Power complete the line loss determination model and report on its progress on a quarterly basis until the project is complete. The SBA raised concerns about the accu...

AI summary Concerns were raised about the accuracy of EV and space heating forecasts, the incorporation of SGNS project data, and the adequacy of demand response (DR) capacity. EOne recommended updating heat pump models and exploring electrification scenarios with electric thermal storage (ETS). Mr. Wilson requested NS Power to complete a line loss determination model and report progress quarterly.

n process, as well as a scenario analysis for ETS to moderate peak load. It will monitor growth in heating load for commercial customers and work with EOne on possible firm peak mitigation strategies. NS Power confirmed that the model used...

AI summary NS Power discusses heat pump efficiency (COP 2.3 at -15°C), DSM allocation (15% industrial, 85% commercial), and DR strategies. It rejects intervenors' requests for line loss modeling, ELCC for LIIR, and commercial electrification analysis. The IRP Action Plan includes lock-out temperature analysis removal.